Predicting mortality in intensive care patients with acute renal failure treated with dialysis.

Existing prognostic methods were compared in their ability to predict mortality in intensive care unit (ICU) patients on dialysis for acute renal failure (ARF). The clinical goal of this study was to determine whether these models could identify a group of patients where dialysis would provide no benefit because of a near 100% certainty of death even with dialysis treatment. This retrospective cohort study included 238 adult patients who received a first dialysis treatment for ARF in the ICU. This study examined the performance of seven general ICU mortality prediction models and four mortality prediction models developed for patients with ARF. These models were assessed for their ability to discriminate mortality form survival and for their ability to calibrate the observed mortality rate with the expected mortality rate. The observed in hospital mortality was 76% for our patient group. Areas under the receiver operating characteristic curve ranged from 0.50 to 0.78. With the Acute Physiology and Chronic Health Evaluation (APACHE) III and the Liano models, the observed mortality in the highest quintiles of risk were 97% and 98%. In conclusion, although none of the models examined in this study showed excellent discrimination between those patients who died in hospital and those who did not, some models (APACHE III, Liano) were able to identify a group of patients with a near 100% chance of mortality. This indicates that these models may have some use in supporting the decision not to initiate dialysis in a subgroup of patients.

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A Comparison of Intensive Care Unit Mortality Prediction Models through the Use of Data Mining Techniques

Healthcare Informatics Research ◽

10.4258/hir.2011.17.4.232 ◽

2011 ◽

Vol 17 (4) ◽

pp. 232 ◽

Cited By ~ 66

Author(s):

Sujin Kim ◽

Woojae Kim ◽

Rae Woong Park

Keyword(s):

Data Mining ◽

Intensive Care Unit ◽

Intensive Care ◽

Intensive Care Unit Mortality ◽

Prediction Models ◽

Mortality Prediction ◽

Data Mining Techniques ◽

Use Of Data ◽

Mortality Prediction Models

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Development of Severity and Mortality Prediction Models for COVID-19 Patients at Emergency Department Including the Chest X-Ray

10.21203/rs.3.rs-88282/v1 ◽

2020 ◽

Author(s):

Pilar Calvillo Batllés ◽

Leonor Cerdá-Alberich ◽

Carles Fonfría-Esparcia ◽

Ainhoa Carreres-Ortega ◽

Carlos Francisco Muñoz-Núñez ◽

...

Keyword(s):

Emergency Department ◽

Intensive Care ◽

Lymphocyte Count ◽

Prediction Models ◽

Mortality Prediction ◽

X Ray ◽

Extent Score ◽

Mortality Prediction Models ◽

Chest X Ray ◽

D Dimer

Abstract Objectives: To develop prognosis prediction models for COVID-19 patients attending an emergency department (ED) based on initial chest X-ray (CXR), demographics, clinical and laboratory parameters. Methods: All symptomatic confirmed COVID-19 patients admitted to our hospital ED between February 24th and April 24th 2020 were recruited. CXR features, clinical and laboratory variables and CXR abnormality indices extracted by a convolutional neural network (CNN) diagnostic tool were considered potential predictors on this first visit. The most serious individual outcome defined the three severity level: 0) home discharge or hospitalization ≤ 3 days, 1) hospital stay >3 days and 2) intensive care requirement or death. Severity and in-hospital mortality multivariable prediction models were developed and internally validated. The Youden index was used for model selection.Results: A total of 440 patients were enrolled (median 64 years; 55.9% male); 13.6% patients were discharged, 64% hospitalized, 6.6% required intensive care and 15.7% died. The severity prediction model included oxygen saturation/inspired oxygen fraction (SatO2/FiO2), age, C-reactive protein (CRP), lymphocyte count, extent score of lung involvement on CXR (ExtScoreCXR), lactate dehydrogenase (LDH), D-dimer level and platelets count, with AUC-ROC=0.94 and AUC-PRC=0.88. The mortality prediction model included age, SatO2/FiO2, CRP, LDH, CXR extent score, lymphocyte count and D-dimer level, with AUC-ROC=0.97 and AUC-PRC=0.78. The addition of CXR CNN-based indices slightly improved the predictive metrics for mortality (AUC-ROC=0.97 and AUC-PRC=0.83).Conclusion: The developed and internally validated severity and mortality prediction models could be useful as triage tools for COVID-19 patients and they should be further validated at different ED.

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Mortality prediction models for pediatric intensive care: comparison of overall and subgroup specific performance

Intensive Care Medicine ◽

10.1007/s00134-013-2857-4 ◽

2013 ◽

Vol 39 (5) ◽

pp. 942-950 ◽

Cited By ~ 31

Author(s):

Idse H. E. Visser ◽

Jan A. Hazelzet ◽

Marcel J. I. J. Albers ◽

Carin W. M. Verlaat ◽

Karin Hogenbirk ◽

...

Keyword(s):

Intensive Care ◽

Prediction Models ◽

Pediatric Intensive Care ◽

Mortality Prediction ◽

Specific Performance ◽

Mortality Prediction Models

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Mortality prediction models, causal effects, and end-of-life decision making in the intensive care unit

BMJ Health & Care Informatics ◽

10.1136/bmjhci-2020-100220 ◽

2020 ◽

Vol 27 (3) ◽

pp. e100220

Author(s):

Jason H Maley ◽

Kerollos N Wanis ◽

Jessica G Young ◽

Leo A Celi

Keyword(s):

Intensive Care Unit ◽

Decision Making ◽

Intensive Care ◽

End Of Life ◽

Prediction Models ◽

Mortality Prediction ◽

Causal Effects ◽

Mortality Prediction Models ◽

End Of Life Decision ◽

Life Decision

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Assessment of performance and utility of mortality prediction models in a single Indian mixed tertiary intensive care unit

International Journal of Critical Illness and Injury Science ◽

10.4103/2229-5151.128010 ◽

2014 ◽

Vol 4 (1) ◽

pp. 29 ◽

Cited By ~ 3

Author(s):

ShardaN Bapat ◽

PracheeM Sathe

Keyword(s):

Intensive Care Unit ◽

Intensive Care ◽

Prediction Models ◽

Mortality Prediction ◽

Assessment Of Performance ◽

Mortality Prediction Models

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Mortality prediction models in intensive care

Critical Care Medicine ◽

10.1097/00003246-199102000-00014 ◽

1991 ◽

Vol 19 (2) ◽

pp. 191-197 ◽

Cited By ~ 40

Author(s):

XAVIER CASTELLA ◽

JAUME GILABERT ◽

FRANCESC TORNER ◽

CARLES TORRES

Keyword(s):

Intensive Care ◽

Prediction Models ◽

Mortality Prediction ◽

Mortality Prediction Models

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Piloting a model-to-data approach to enable predictive analytics in health care through patient mortality prediction

Journal of the American Medical Informatics Association ◽

10.1093/jamia/ocaa083 ◽

2020 ◽

Vol 27 (9) ◽

pp. 1393-1400

Author(s):

Timothy Bergquist ◽

Yao Yan ◽

Thomas Schaffter ◽

Thomas Yu ◽

Vikas Pejaver ◽

...

Keyword(s):

Predictive Models ◽

Performance Metrics ◽

Prediction Models ◽

Predictive Analytics ◽

Characteristic Curve ◽

Mortality Prediction ◽

Patient Privacy ◽

University Of Washington ◽

Mortality Prediction Models ◽

The University

Abstract Objective The development of predictive models for clinical application requires the availability of electronic health record (EHR) data, which is complicated by patient privacy concerns. We showcase the “Model to Data” (MTD) approach as a new mechanism to make private clinical data available for the development of predictive models. Under this framework, we eliminate researchers’ direct interaction with patient data by delivering containerized models to the EHR data. Materials and Methods We operationalize the MTD framework using the Synapse collaboration platform and an on-premises secure computing environment at the University of Washington hosting EHR data. Containerized mortality prediction models developed by a model developer, were delivered to the University of Washington via Synapse, where the models were trained and evaluated. Model performance metrics were returned to the model developer. Results The model developer was able to develop 3 mortality prediction models under the MTD framework using simple demographic features (area under the receiver-operating characteristic curve [AUROC], 0.693), demographics and 5 common chronic diseases (AUROC, 0.861), and the 1000 most common features from the EHR’s condition/procedure/drug domains (AUROC, 0.921). Discussion We demonstrate the feasibility of the MTD framework to facilitate the development of predictive models on private EHR data, enabled by common data models and containerization software. We identify challenges that both the model developer and the health system information technology group encountered and propose future efforts to improve implementation. Conclusions The MTD framework lowers the barrier of access to EHR data and can accelerate the development and evaluation of clinical prediction models.

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